他把每個詞語孤立起來，該網絡如果想在下面一個句子中填入一個單詞，就不會根據apple聯想到orange

所以就希望能夠使用向量化的方式來表示單詞：

這樣Apple和Orange就會有相似的地方，在這個特征空間內會距離比較近。
而且還有這樣的特性：

如何學習到這個詞嵌入矩陣：

我們建立一個神經網絡像上圖那樣用前面幾個詞 預測後面一個詞
通過誤差反向傳播就學會了 E矩陣
代碼如下：

# coding: utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import math
import os
import random
import zipfile

import numpy as np
from six.moves import urllib
from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf
import pickle

# Step 1: Download the data.
url = ‘http://mattmahoney.net/dc/‘

# 下載數據集
def maybe_download(filename, expected_bytes):
    """Download a file if not present, and make sure it‘s the right size."""
    if not os.path.exists(filename):
        filename, _ = urllib.request.urlretrieve(url + filename, filename)
    # 獲取文件相關屬性
    statinfo = os.stat(filename)
    # 比對文件的大小是否正確
    if statinfo.st_size == expected_bytes:
        print(‘Found and verified‘, filename)
    else:
        print(statinfo.st_size)
        raise Exception(
            ‘Failed to verify ‘ + filename + ‘. Can you get to it with a browser?‘)
    return filename

filename = maybe_download(‘text8.zip‘, 31344016)

# Read the data into a list of strings.
def read_data(filename):
    """Extract the first file enclosed in a zip file as a list of words"""
    with zipfile.ZipFile(filename) as f:
        data = tf.compat.as_str(f.read(f.namelist()[0])).split()
    return data

# 單詞表
words = read_data(filename)

# Data size
print(‘Data size‘, len(words))

# Step 2: Build the dictionary and replace rare words with UNK token.
# 只留50000個單詞，其他的詞都歸為UNK
vocabulary_size = 50000

def build_dataset(words, vocabulary_size):
    count = [[‘UNK‘, -1]]
    # extend追加一個列表
    # Counter用來統計每個詞出現的次數
    # most_common返回一個TopN列表，只留50000個單詞包括UNK  
    # c = Counter(‘abracadabra‘)
    # c.most_common()
    # [(‘a‘, 5), (‘r‘, 2), (‘b‘, 2), (‘c‘, 1), (‘d‘, 1)]
    # c.most_common(3)
    # [(‘a‘, 5), (‘r‘, 2), (‘b‘, 2)]
    # 前50000個出現次數最多的詞
    count.extend(collections.Counter(words).most_common(vocabulary_size - 1))
    # 生成 dictionary，詞對應編號, word:id(0-49999)
    # 詞頻越高編號越小
    dictionary = dict()
    for word, _ in count:
        dictionary[word] = len(dictionary)
    # data把數據集的詞都編號
    data = list()
    unk_count = 0
    for word in words:
        if word in dictionary:
            index = dictionary[word]
        else:
            index = 0  # dictionary[‘UNK‘]
            unk_count += 1
        data.append(index)
    # 記錄UNK詞的數量
    count[0][1] = unk_count
    # 編號對應詞的字典
    reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys()))
    return data, count, dictionary, reverse_dictionary

# data 數據集，編號形式
# count 前50000個出現次數最多的詞
# dictionary 詞對應編號
# reverse_dictionary 編號對應詞
data, count, dictionary, reverse_dictionary = build_dataset(words, vocabulary_size)
del words  # Hint to reduce memory.
print(‘Most common words (+UNK)‘, count[:5])
print(‘Sample data‘, data[:10], [reverse_dictionary[i] for i in data[:10]])

data_index = 0

# Step 3: Function to generate a training batch for the skip-gram model.
def generate_batch(batch_size, num_skips, skip_window):
    global data_index
    assert batch_size % num_skips == 0
    assert num_skips <= 2 * skip_window

    batch = np.ndarray(shape=(batch_size), dtype=np.int32)
    labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)

    span = 2 * skip_window + 1  # [ skip_window target skip_window ]
    buffer = collections.deque(maxlen=span)
    # [ skip_window target skip_window ]
            # [ skip_window target skip_window ]
                    # [ skip_window target skip_window ]

#     [0 1 2 3 4 5 6 7 8 9 ...]
#            t     i  
    # 循環3次
    for _ in range(span):
        buffer.append(data[data_index])
        data_index = (data_index + 1) % len(data)
    # 獲取batch和labels
    for i in range(batch_size // num_skips):
        target = skip_window  # target label at the center of the buffer
        targets_to_avoid = [skip_window]
        # 循環2次，一個目標單詞對應兩個上下文單詞
        for j in range(num_skips):
            while target in targets_to_avoid:
                # 可能先拿到前面的單詞也可能先拿到後面的單詞
                target = random.randint(0, span - 1)
            targets_to_avoid.append(target)
            batch[i * num_skips + j] = buffer[skip_window]
            labels[i * num_skips + j, 0] = buffer[target]
        buffer.append(data[data_index])
        data_index = (data_index + 1) % len(data)
    # Backtrack a little bit to avoid skipping words in the end of a batch
    # 回溯3個詞。因為執行完一個batch的操作之後，data_index會往右多偏移span個位置
    data_index = (data_index + len(data) - span) % len(data)
    return batch, labels

# 打印sample data
batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)
for i in range(8):
    print(batch[i], reverse_dictionary[batch[i]],
        ‘->‘, labels[i, 0], reverse_dictionary[labels[i, 0]])

# Step 4: Build and train a skip-gram model.
batch_size = 128
# 詞向量維度
embedding_size = 128  # Dimension of the embedding vector.
skip_window = 1       # How many words to consider left and right.
num_skips = 2         # How many times to reuse an input to generate a label.

# We pick a random validation set to sample nearest neighbors. Here we limit the
# validation samples to the words that have a low numeric ID, which by
# construction are also the most frequent.
valid_size = 16     # Random set of words to evaluate similarity on.
valid_window = 100  # Only pick dev samples in the head of the distribution.
# 從0-100抽取16個整數，無放回抽樣
valid_examples = np.random.choice(valid_window, valid_size, replace=False) 
# 負采樣樣本數
num_sampled = 64    # Number of negative examples to sample.

graph = tf.Graph()
with graph.as_default():
    # Input data.
    train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
    train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
    valid_dataset = tf.constant(valid_examples, dtype=tf.int32)

    # Ops and variables pinned to the CPU because of missing GPU implementation
#     with tf.device(‘/cpu:0‘):
        # 詞向量
        # Look up embeddings for inputs.
    embeddings = tf.Variable(
        tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
    # embedding_lookup(params,ids)其實就是按照ids順序返回params中的第ids行
    # 比如說，ids=[1,7,4],就是返回params中第1,7,4行。返回結果為由params的1,7,4行組成的tensor
    # 提取要訓練的詞
    embed = tf.nn.embedding_lookup(embeddings, train_inputs)

    # Construct the variables for the noise-contrastive estimation(NCE) loss
    nce_weights = tf.Variable(
        tf.truncated_normal([vocabulary_size, embedding_size],
                        stddev=1.0 / math.sqrt(embedding_size)))
    nce_biases = tf.Variable(tf.zeros([vocabulary_size]))

    # Compute the average NCE loss for the batch.
    # tf.nce_loss automatically draws a new sample of the negative labels each
    # time we evaluate the loss.
    loss = tf.reduce_mean(
        tf.nn.nce_loss(weights=nce_weights,
                       biases=nce_biases,
                       labels=train_labels,
                       inputs=embed,
                       num_sampled=num_sampled,   
                       num_classes=vocabulary_size))

    # Construct the SGD optimizer using a learning rate of 1.0.
    optimizer = tf.train.GradientDescentOptimizer(1).minimize(loss)

    # Compute the cosine similarity between minibatch examples and all embeddings.
    norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))
    normalized_embeddings = embeddings / norm
    # 抽取一些常用詞來測試余弦相似度
    valid_embeddings = tf.nn.embedding_lookup(
        normalized_embeddings, valid_dataset)
    # valid_size == 16
    # [16,1] * [1*50000] = [16,50000]
    similarity = tf.matmul(
        valid_embeddings, normalized_embeddings, transpose_b=True)

    # Add variable initializer.
    init = tf.global_variables_initializer()

# Step 5: Begin training.
num_steps = 100001
final_embeddings = []

with tf.Session(graph=graph) as session:
    # We must initialize all variables before we use them.
    init.run()
    print("Initialized")

    average_loss = 0
    for step in xrange(num_steps):
        # 獲取一個批次的target，以及對應的labels，都是編號形式的
        batch_inputs, batch_labels = generate_batch(
            batch_size, num_skips, skip_window)
        feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}

        # We perform one update step by evaluating the optimizer op (including it
        # in the list of returned values for session.run()
        _, loss_val = session.run([optimizer, loss], feed_dict=feed_dict)
        average_loss += loss_val

        # 計算訓練2000次的平均loss
        if step % 2000 == 0:
            if step > 0:
                average_loss /= 2000
            # The average loss is an estimate of the loss over the last 2000 batches.
            print("Average loss at step ", step, ": ", average_loss)
            average_loss = 0

        # Note that this is expensive (~20% slowdown if computed every 500 steps)
        if step % 20000 == 0:
            sim = similarity.eval()
            # 計算驗證集的余弦相似度最高的詞
            for i in xrange(valid_size):
                # 根據id拿到對應單詞
                valid_word = reverse_dictionary[valid_examples[i]]
                top_k = 8  # number of nearest neighbors
                # 從大到小排序，排除自己本身，取前top_k個值
                nearest = (-sim[i, :]).argsort()[1:top_k + 1]
                log_str = "Nearest to %s:" % valid_word
                for k in xrange(top_k):
                    close_word = reverse_dictionary[nearest[k]]
                    log_str = "%s %s," % (log_str, close_word)
                print(log_str)
    # 訓練結束得到的詞向量
    final_embeddings = normalized_embeddings.eval()

# Step 6: Visualize the embeddings.

def plot_with_labels(low_dim_embs, labels, filename=‘tsne.png‘):
    assert low_dim_embs.shape[0] >= len(labels), "More labels than embeddings"
    # 設置圖片大小
    plt.figure(figsize=(15, 15))  # in inches
    for i, label in enumerate(labels):
        x, y = low_dim_embs[i, :]
        plt.scatter(x, y)
        plt.annotate(label,
                 xy=(x, y),
                 xytext=(5, 2),
                 textcoords=‘offset points‘,
                 ha=‘right‘,
                 va=‘bottom‘)

    plt.savefig(filename)

try:
    from sklearn.manifold import TSNE
    import matplotlib.pyplot as plt

    tsne = TSNE(perplexity=30, n_components=2, init=‘pca‘, n_iter=5000, method=‘exact‘)# mac：method=‘exact‘
    # 畫500個點
    plot_only = 500
    low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])
    labels = [reverse_dictionary[i] for i in xrange(plot_only)]
    plot_with_labels(low_dim_embs, labels)

except ImportError:
    print("Please install sklearn, matplotlib, and scipy to visualize embeddings.")
 

如何學習word2vec